What is AutoML and Why You Should Use It?

Automated machine learning is a key concept, enabling domain specialists to leverage data to enhance tasks, streamline repetitive processes, and make machine learning accessible to everyone.

According to Gartner, as of 2023, the Automated Machine Learning (AutoML) market holds a value of $1 billion. By 2028, it’s anticipated to surge to around $6.4 billion. This significant growth trajectory highlights the crucial role of AutoML in driving advancements in machine learning and automation technologies.

This article aims to thoroughly examine the AutoML definition, highlighting its process, advantages, and practical real-life applications; let’s begin!

I. What is AutoML?

Automated machine learning (AutoML) streamlines the application of machine learning models to solve real-world challenges by automating the process. It specifically focuses on automating the selection, composition, and tuning of ML models. This automation not only makes the machine learning process more accessible but also tends to deliver quicker and more precise results compared to manually coded algorithms.

AutoML meaning they simplify the use of machine learning, enabling organizations that lack specialized data scientists or ML experts to harness the power of machine learning. These platforms are available through various channels: they can be purchased from third-party vendors, sourced from open-source platforms like GitHub, or developed internally.

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II. Benefits of AutoML

AutoML’s integration into services brings forth a range of key advantages, reshaping the industry. These benefits include significant time reduction, improved accessibility, increased transparency, boosted efficiency, better performance tuning, and making AI more universally accessible. Let’s delve into these AutoML advantages.

Time Efficiency	AutoML software streamlines the process of feeding data into the training algorithm and autonomously identifies the optimal neural network structure for specific problems. The time taken depends on the data volume and the number of models used. For example, basic structured datasets might take mere seconds. However, the time could extend to days or even weeks for larger datasets or when evaluating multiple model variations with different algorithms. Despite this, AutoML is known for speeding up deployment by automating data handling and algorithm selection, thus significantly reducing the time needed for deployment.
Enhanced Accessibility	AutoML reduces entry barriers for model creation, enabling sectors previously sidelined to leverage machine learning advantages. This fosters innovation, intensifies market competition, and drives progress. Expanding machine learning across various sectors improves productivity and effectiveness where it’s most needed.
Greater Transparency	As companies expand, industry trends shift, and data volumes grow, AutoML leads to better models by reducing bias or human error. AutoML ranks models based on performance, offering quick insights into the model’s construction and components. This benefit allows companies to innovate confidently, ensuring greater transparency, tangible business benefits, and a higher return on investment.
Increased Efficiency	AutoML makes applying machine learning to practical problems less complex. It simplifies creating, testing, and deploying machine learning frameworks, addressing business issues more efficiently and productively. AutoML cycles through different models and optimizes hyperparameters, delivering high-performance models that would otherwise take considerable time to develop manually.
Optimized Performance	AutoML enables companies to create and implement highly accurate predictive models quickly. This facilitates data-driven decisions, optimizes resource distribution, and reduces operational inefficiencies. By automating intricate processes, AutoML improves forecasting precision, leading to better inventory management and decreased revenue loss from stock shortages.
AI Democratization	The demand for advanced ML knowledge is increasing, but the availability of experts needs to catch up. The U.S. Bureau of Labor Statistics predicts a significant job growth rate in data science skills by 2026. AutoML helps fill this gap by automating processes that might otherwise be too complex for non-experts. With automation, AI development and machine learning software have become more user-friendly and accessible. This allows analysts, marketers, and IT staff without a data science background to integrate AI and ML into their routine tasks easily.

III. How does AutoML work?

AutoML enhances the efficiency of the machine learning workflow by automating mundane tasks, thereby simplifying its intricate phases. Typically, this procedure involves the steps outlined below:

Step 1. Data Preprocessing and Exploration

In this initial phase, the automation process in machine learning involves recognizing types of columns, transforming them into numerical data, and addressing missing values. It also entails pinpointing variables with minimal predictive value and removing highly interrelated ones.

Step 2. Extraction of Features

At this juncture, tasks related to feature engineering and normalization are executed. Notably, features with significant predictive capability are selected automatically from a broad set of features. This step refines the data, making it more suitable for training machine learning models and thereby enhancing the model’s precision.

Step 3. Selection of Model and Tuning of Hyperparameters

The AutoML framework employs techniques and hyperparameters to train multiple machine-learning models with the preprocessed data. This enables the framework to determine the most effective model for the given data.

Step 4. Preparation for Deployment

Following the assessment of competing models based on specific metrics, users are equipped to utilize the trained model for making predictions or taking actions on new, unprocessed data. This is commonly achieved by launching the model as a web service, thus making it accessible to other users or systems.

While AutoML boasts impressive functionalities and can automate the development process, it should not be misconstrued as a complete substitute for data scientists. Rather, it should be viewed as a broad search strategy furnished with advanced search algorithms designed to find the best solutions for each segment of the machine-learning pipeline.

In practice, data scientists remain integral for tasks such as designing experiments, interpreting outcomes, and overseeing models throughout their entire lifecycle.

IV. AutoML Use Cases

Here are a few practical use cases demonstrating how automated machine learning is being utilized effectively across various industries:

• Healthcare: Identifying Candidates for Liver Transplant

The University of Pittsburgh Medical Center (UPMC), with its expansive network of 40 hospitals, offers a range of specialized services, including living donor liver transplants (LDLT), boasting the largest LDLT program in the US.

Many patients in need of liver transplants are not informed about the LDLT option. Previously, UPMC had to engage with 10,000 individuals to identify a single potential patient, filtering through 1,500 criteria. Adopting the Squark platform, which specializes in low-code AutoML solutions, transformed this process.

A fundamental understanding of statistics is required to develop and train ML models that pinpoint individuals interested in LDLT services. The outreach has dramatically reduced from 10,000 to 75 candidates, and model development time has been reduced from months to hours.

• Recruitment: Filtering Out Incompatible Resumes

The Adecco Group, the world’s second-largest H.R provider, leverages machine learning to expedite the job-filling process. By replacing traditional manual methods with AutoML, the company initiated 60 ML projects using 3,000 models within three weeks. The most efficient models eliminate about 37 percent of resumes unsuitable for the job, saving recruiters’ time and enhancing their productivity by 10 percent.

• Telecommunications: Anticipating Equipment Malfunctions

Mobile Broadband Network LTD (MBNL) is a prominent telecommunications service provider co-owned by two of Britain’s most innovative mobile operators.

MBNL oversees 22,000 network towers throughout the UK. To preempt failures between routine inspections, MBNL adopted AI-driven predictive maintenance, supported by

AutoML. This automated method enabled the completion of a proof-of-concept in just six weeks, a task that typically takes one to two years.

ML algorithms predict more than 50 percent of air conditioning malfunctions a month in advance. Such breakdowns can cause overheating and disrupt services. MBNL utilizes these forecasts to plan maintenance work and avoid expensive service interruptions.

• Financial Services: Identifying Fraudulent Transactions

Mastercard has developed an AI and machine learning-based system called Decision Intelligence. This system uses AutoML to analyze transaction data in real time, providing a more accurate assessment of whether a transaction is genuine or fraudulent. This reduces false declines and improves the overall customer experience.

Conclusion

AutoML is revolutionizing industries by making machine learning more accessible and efficient. From healthcare to finance, it enables faster, data-driven decisions and transforms operations. However, harnessing AutoML’s full potential requires expertise.

This is where TECHVIFY excels. Our specialized AI/ML services are designed to seamlessly integrate them into your business, ensuring you stay ahead in this automated era. Ready to leverage the power of AI & Machine Learning? Contact TECHVIFY for top-tier AI solutions and propel your business forward.